Unsupervised Deep Embedding for Clustering Analysis

Code

• github.com/piiswrong/dec
  OfficialIn papermxnet★ 0
• github.com/DIDSR/DomId
  pytorch★ 13
• github.com/probabilistic-and-interactive-ml/breaking-the-reclustering-barrier
  pytorch★ 12
• github.com/Derek-Wds/MAD-VAE
  pytorch★ 5
• github.com/gdec-submission/gdec
  pytorch★ 4
• github.com/yamilvindas/gdec
  pytorch★ 4
• github.com/yamilvindas/imbalanced_dec_regularization
  pytorch★ 0
• github.com/elitalobo/Hierarchical-RL-Algorithms
  pytorch★ 0
• github.com/vlukiyanov/pt-dec
  pytorch★ 0
• github.com/AhmedYounes94/Semi-supervised-BERT-NER
  tf★ 0

Abstract

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.

Tasks

Benchmark Results

Reproductions